Project Summary/Abstract The goal of the project is to develop a model that explains how decisions are made about information that is continuously distributed across space, with decisions made on continuous scales. The model will provide a unified explanation of the full range of decision-making data, including accuracy, the distributions of response times for correct and incorrect responses, how they change with manipulations of independent variables, and how they differ among individuals and groups of individuals. The totality of these data will place severe constraints on the development of the model and its success. The tasks used to test the model will ask participants to make simple decisions quickly; for example, point to the area on a circle that is the brightest. Brightness is a continuous scale and responses are made on a continuous scale (the circle). Large, comprehensive bodies of data will be collected for decision-making in perception, long-term memory, working memory, and numeracy. Statistical properties of the model will be examined and the numbers of observations needed will be determined for experiments with clinical patients, children, and other populations for whom the time for testing must be short. Four populations of adults will be studied: young adults, adults with Mild Cognitive Impairment (MCI), adults with early Alzheimer's Disease (AD), and older adults who have no cognitive impairments. The aim will be to understand how normal aging affects individual components of processing in continuous decision-making and how MCI and early AD affect the components. Almost no research has been conducted with AD and MCI patients that models the time course of decision-making and no research has been done for decisions made on continuous response scales about continuously distributed information. It is not known if the components of decision-making are the same for MCI and AD patients as for unimpaired older adults and it is not known how independent variables (e.g., the difficulty of a task) affect performance for MCI and AD patients. It is also not known whether a modeling approach can uncover preserved skills not discernible from accuracy and RT data alone.